Multi-Agent Deep RL for Efficient Task Offloading in Vehicle Edge Computing

The evolution of Vehicle Edge Computing (VEC) within the Internet of Vehicles (IoV) has revolutionized vehicular technology, introducing computational capabilities into vehicles and enabling a spectrum of intelligent applications. However, VEC faces challenges, notably in executing computationally demanding tasks, leading to increased latency and energy consumption due to limited onboard computing capacities. Addressing these challenges demands innovative solutions. Deep Reinforcement Learning (DRL), particularly in a multi-agent framework, emerges as a promising solution to navigate the complexities of VEC. Multi-agent DRL holds the potential to optimize task offloading strategies in dynamic VEC environments, mitigating latency issues and enhancing energy efficiency. This paper introduces a novel approach, presenting a tailored multi-agent DRL model crafted explicitly for efficient task offloading in VEC settings. The subsequent sections explore VEC literature, delve into DRL methodologies, detail the proposed model, present empirical findings, and offer implications. This comprehensive investigation aims to elucidate the potential of multi-agent DRL in enhancing task offloading efficiency within VEC, steering advancements in this burgeoning research field.